Phenomenological Study Of NMSSM By Using Machine Learning Scanning Method

At present,the standard model is being challenged by more and more experiments,such as dark matter frequently mentioned in cosmology,the problem of muon abnormal magnetic moment,and the problem of neutrino mass.Therefore,further breakthroughs are needed in basic physics,among them,the supersymmetric model and its derivative model is a very popular breakthrough.However,in order to break through basic physics,in addition to proposing new models and new theories,corresponding phenomenological research should also be carried out.Particle physics phenomenology is a very important link in basic physics research.As a bridge between theory and experiment,the study of particle physics phenomenology can verify the correctness of the theory,and dock the theoretical prediction and experimental results.The work of phenomenology is to calculate and simulate the new model,compare the experimental results and improve the details of the model.This process requires a lot of computational costs,so phenomenology research is also a technical work.In recent years,machine learning technology has developed on a large scale in the field of computer science and applications.At the same time,it has been introduced into scientific research by various basic science disciplines and has received gratifying feedback.In physics,cosmology,materials science,and condensed matter physics are actively introducing machine learning technology to assist research,but particle physics has not had many similar explorations.Therefore,this paper will be based on the Next-to-Minimal Supersymmetric Standard Model(NMSSM),mainly from the perspective of phenomenology of dark matter physics,taking into account the muon abnormal magnetic moment and 125 Ge V higgs limit,to explore the application of machine learning technology in particle physics phenomenology research.